Chicago, IL, United States of America

Gregory Filla


Average Co-Inventor Count = 6.0

ph-index = 1

Forward Citations = 1(Granted Patents)


Company Filing History:


Years Active: 2024

where 'Filed Patents' based on already Granted Patents

1 patent (USPTO):

Title: Gregory Filla: Innovator in AI Pipeline Development

Introduction

Gregory Filla is a notable inventor based in Chicago, IL, who has made significant contributions to the field of artificial intelligence. His innovative work focuses on enhancing the efficiency and usability of AI pipelines through advanced coding techniques.

Latest Patents

Filla holds a patent for "Code generation for Auto-AI." This invention encompasses a method, computer program product, and system designed to create a data analysis tool. The method involves a computing device that generates an AI pipeline based on an input dataset, utilizing an Automated Machine Learning program. A key feature of this invention is the conversion of the AI pipeline to a non-native format of the Automated Machine Learning program. This conversion allows the AI pipeline to be utilized outside of the Automated Machine Learning program, thereby increasing its overall usefulness. Additionally, this innovation enhances the efficiency of running the AI pipeline by eliminating unnecessary computations performed by the Automated Machine Learning program.

Career Highlights

Gregory Filla is currently employed at International Business Machines Corporation, commonly known as IBM. His role at IBM allows him to work on cutting-edge technologies and contribute to advancements in AI and machine learning.

Collaborations

Filla collaborates with talented individuals in his field, including coworkers Peter Daniel Kirchner and Gregory Bramble. Their combined expertise fosters an environment of innovation and creativity.

Conclusion

Gregory Filla's contributions to AI pipeline development exemplify the impact of innovative thinking in technology. His patent for code generation in Auto-AI showcases his commitment to enhancing the efficiency and usability of AI systems.

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